Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations3276
Missing cells1434
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory419.2 KiB
Average record size in memory131.0 B

Variable types

Numeric9
Categorical1

Alerts

pH has 491 (15.0%) missing valuesMissing
Sulfatos has 781 (23.8%) missing valuesMissing
Trihalometanos has 162 (4.9%) missing valuesMissing
Sólidos has unique valuesUnique

Reproduction

Analysis started2024-09-22 22:51:58.638335
Analysis finished2024-09-22 22:52:05.009981
Duration6.37 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

pH
Real number (ℝ)

MISSING 

Distinct699
Distinct (%)25.1%
Missing491
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean7.0808043
Minimum0
Maximum14
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:05.068995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.49
Q16.09
median7.04
Q38.06
95-th percentile9.788
Maximum14
Range14
Interquartile range (IQR)1.97

Descriptive statistics

Standard deviation1.5943694
Coefficient of variation (CV)0.22516784
Kurtosis0.71974569
Mean7.0808043
Median Absolute Deviation (MAD)0.99
Skewness0.025748718
Sum19720.04
Variance2.5420137
MonotonicityNot monotonic
2024-09-22T17:52:05.170017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.28 15
 
0.5%
6.62 14
 
0.4%
6.58 14
 
0.4%
7.61 14
 
0.4%
7.37 14
 
0.4%
6.92 13
 
0.4%
6.45 13
 
0.4%
6.73 12
 
0.4%
6.85 12
 
0.4%
6.9 12
 
0.4%
Other values (689) 2652
81.0%
(Missing) 491
 
15.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.23 1
< 0.1%
0.98 1
< 0.1%
0.99 1
< 0.1%
1.43 1
< 0.1%
1.76 1
< 0.1%
1.84 1
< 0.1%
1.99 1
< 0.1%
2.13 1
< 0.1%
2.38 1
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
13.54 1
< 0.1%
13.35 1
< 0.1%
13.18 1
< 0.1%
12.25 1
< 0.1%
11.91 1
< 0.1%
11.9 1
< 0.1%
11.62 1
< 0.1%
11.57 1
< 0.1%
11.56 1
< 0.1%

Dureza
Real number (ℝ)

Distinct2835
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.36948
Minimum47.43
Maximum323.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:05.268953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum47.43
5-th percentile141.7625
Q1176.8475
median196.965
Q3216.67
95-th percentile249.61
Maximum323.12
Range275.69
Interquartile range (IQR)39.8225

Descriptive statistics

Standard deviation32.87968
Coefficient of variation (CV)0.16743783
Kurtosis0.61580528
Mean196.36948
Median Absolute Deviation (MAD)19.845
Skewness-0.039345376
Sum643306.42
Variance1081.0734
MonotonicityNot monotonic
2024-09-22T17:52:05.365591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211.45 5
 
0.2%
199.08 4
 
0.1%
208.91 4
 
0.1%
200.71 4
 
0.1%
179.15 4
 
0.1%
203.2 3
 
0.1%
185.34 3
 
0.1%
185.93 3
 
0.1%
168.28 3
 
0.1%
216.12 3
 
0.1%
Other values (2825) 3240
98.9%
ValueCountFrequency (%)
47.43 1
< 0.1%
73.49 1
< 0.1%
77.46 1
< 0.1%
81.71 1
< 0.1%
94.09 1
< 0.1%
94.81 1
< 0.1%
94.91 1
< 0.1%
97.28 1
< 0.1%
98.37 1
< 0.1%
98.45 1
< 0.1%
ValueCountFrequency (%)
323.12 1
< 0.1%
317.34 1
< 0.1%
311.38 1
< 0.1%
308.25 1
< 0.1%
307.71 1
< 0.1%
306.63 1
< 0.1%
304.24 1
< 0.1%
303.7 1
< 0.1%
300.29 1
< 0.1%
298.1 1
< 0.1%

Sólidos
Real number (ℝ)

UNIQUE 

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22014.092
Minimum320.94
Maximum61227.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:05.463613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum320.94
5-th percentile9545.8125
Q115666.688
median20927.83
Q327332.76
95-th percentile38474.988
Maximum61227.2
Range60906.26
Interquartile range (IQR)11666.073

Descriptive statistics

Standard deviation8768.5709
Coefficient of variation (CV)0.39831626
Kurtosis0.44282614
Mean22014.092
Median Absolute Deviation (MAD)5809.48
Skewness0.62163449
Sum72118167
Variance76887835
MonotonicityNot monotonic
2024-09-22T17:52:05.564636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17404.18 1
 
< 0.1%
20791.32 1
 
< 0.1%
18630.06 1
 
< 0.1%
19909.54 1
 
< 0.1%
22018.42 1
 
< 0.1%
17978.99 1
 
< 0.1%
28748.69 1
 
< 0.1%
28749.72 1
 
< 0.1%
13672.09 1
 
< 0.1%
14285.58 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
320.94 1
< 0.1%
728.75 1
< 0.1%
1198.94 1
< 0.1%
1351.91 1
< 0.1%
1372.09 1
< 0.1%
2552.96 1
< 0.1%
2808.03 1
< 0.1%
2835.3 1
< 0.1%
2912.21 1
< 0.1%
3413.08 1
< 0.1%
ValueCountFrequency (%)
61227.2 1
< 0.1%
56867.86 1
< 0.1%
56488.67 1
< 0.1%
56351.4 1
< 0.1%
56320.59 1
< 0.1%
55334.7 1
< 0.1%
53735.9 1
< 0.1%
52318.92 1
< 0.1%
52060.23 1
< 0.1%
51731.82 1
< 0.1%

Cloraminas
Real number (ℝ)

Distinct722
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1222405
Minimum0.35
Maximum13.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:05.666659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile4.5
Q16.13
median7.13
Q38.1125
95-th percentile9.75
Maximum13.13
Range12.78
Interquartile range (IQR)1.9825

Descriptive statistics

Standard deviation1.5831425
Coefficient of variation (CV)0.22228153
Kurtosis0.58992078
Mean7.1222405
Median Absolute Deviation (MAD)0.99
Skewness-0.012314856
Sum23332.46
Variance2.5063402
MonotonicityNot monotonic
2024-09-22T17:52:05.771682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.7 16
 
0.5%
7.3 16
 
0.5%
7.63 16
 
0.5%
6.61 16
 
0.5%
6.2 15
 
0.5%
6.48 15
 
0.5%
6.91 15
 
0.5%
7.66 14
 
0.4%
6.77 14
 
0.4%
7.57 14
 
0.4%
Other values (712) 3125
95.4%
ValueCountFrequency (%)
0.35 1
< 0.1%
0.53 1
< 0.1%
1.39 1
< 0.1%
1.68 1
< 0.1%
1.92 1
< 0.1%
2.1 1
< 0.1%
2.39 1
< 0.1%
2.4 1
< 0.1%
2.46 2
0.1%
2.48 1
< 0.1%
ValueCountFrequency (%)
13.13 1
< 0.1%
13.04 1
< 0.1%
12.91 1
< 0.1%
12.65 1
< 0.1%
12.63 1
< 0.1%
12.58 1
< 0.1%
12.36 1
< 0.1%
12.28 1
< 0.1%
12.25 1
< 0.1%
12.23 1
< 0.1%

Sulfatos
Real number (ℝ)

MISSING 

Distinct2303
Distinct (%)92.3%
Missing781
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean333.77578
Minimum129
Maximum481.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:05.867703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile266.616
Q1307.695
median333.07
Q3359.95
95-th percentile403.067
Maximum481.03
Range352.03
Interquartile range (IQR)52.255

Descriptive statistics

Standard deviation41.416864
Coefficient of variation (CV)0.12408589
Kurtosis0.64825871
Mean333.77578
Median Absolute Deviation (MAD)26.1
Skewness-0.035938422
Sum832770.58
Variance1715.3566
MonotonicityNot monotonic
2024-09-22T17:52:05.966563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
334.05 4
 
0.1%
350.45 3
 
0.1%
340.98 3
 
0.1%
318.79 3
 
0.1%
329.13 3
 
0.1%
330.13 3
 
0.1%
319.25 3
 
0.1%
320.26 3
 
0.1%
343.29 3
 
0.1%
339.06 3
 
0.1%
Other values (2293) 2464
75.2%
(Missing) 781
 
23.8%
ValueCountFrequency (%)
129 1
< 0.1%
180.21 1
< 0.1%
182.4 1
< 0.1%
187.17 1
< 0.1%
187.42 1
< 0.1%
192.03 1
< 0.1%
203.44 1
< 0.1%
205.94 1
< 0.1%
206.25 1
< 0.1%
207.89 1
< 0.1%
ValueCountFrequency (%)
481.03 1
< 0.1%
476.54 1
< 0.1%
475.74 1
< 0.1%
462.47 1
< 0.1%
460.11 1
< 0.1%
458.44 1
< 0.1%
455.45 1
< 0.1%
450.91 1
< 0.1%
449.27 1
< 0.1%
447.42 1
< 0.1%

Conductividad
Real number (ℝ)

Distinct3099
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean426.20517
Minimum181.48
Maximum753.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:06.063585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum181.48
5-th percentile300.1125
Q1365.735
median421.885
Q3481.79
95-th percentile566.35
Maximum753.34
Range571.86
Interquartile range (IQR)116.055

Descriptive statistics

Standard deviation80.824123
Coefficient of variation (CV)0.18963665
Kurtosis-0.27709133
Mean426.20517
Median Absolute Deviation (MAD)57.89
Skewness0.26448991
Sum1396248.1
Variance6532.5388
MonotonicityNot monotonic
2024-09-22T17:52:06.162608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
402.66 3
 
0.1%
412.71 3
 
0.1%
517.43 3
 
0.1%
344.15 3
 
0.1%
475.3 3
 
0.1%
399.95 3
 
0.1%
355.94 3
 
0.1%
344.72 3
 
0.1%
413 2
 
0.1%
376.67 2
 
0.1%
Other values (3089) 3248
99.1%
ValueCountFrequency (%)
181.48 1
< 0.1%
201.62 1
< 0.1%
210.32 1
< 0.1%
217.36 1
< 0.1%
232.61 1
< 0.1%
233.91 1
< 0.1%
235.04 1
< 0.1%
245.86 1
< 0.1%
247.92 1
< 0.1%
251.02 1
< 0.1%
ValueCountFrequency (%)
753.34 1
< 0.1%
708.23 1
< 0.1%
695.37 1
< 0.1%
674.44 1
< 0.1%
672.56 1
< 0.1%
669.73 1
< 0.1%
666.69 1
< 0.1%
660.25 1
< 0.1%
657.57 1
< 0.1%
656.92 1
< 0.1%

Carbono_orgánico
Real number (ℝ)

Distinct1235
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.284921
Minimum2.2
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:06.263630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.8175
Q112.07
median14.22
Q316.56
95-th percentile19.64
Maximum28.3
Range26.1
Interquartile range (IQR)4.49

Descriptive statistics

Standard deviation3.308232
Coefficient of variation (CV)0.23158911
Kurtosis0.044560751
Mean14.284921
Median Absolute Deviation (MAD)2.23
Skewness0.025606746
Sum46797.4
Variance10.944399
MonotonicityNot monotonic
2024-09-22T17:52:06.475677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.79 10
 
0.3%
13.45 10
 
0.3%
15.67 10
 
0.3%
14.53 9
 
0.3%
14.67 9
 
0.3%
14.25 9
 
0.3%
16.14 8
 
0.2%
13.17 8
 
0.2%
14.17 8
 
0.2%
14.9 8
 
0.2%
Other values (1225) 3187
97.3%
ValueCountFrequency (%)
2.2 1
< 0.1%
4.37 1
< 0.1%
4.47 2
0.1%
4.86 1
< 0.1%
4.9 1
< 0.1%
4.97 1
< 0.1%
5.05 1
< 0.1%
5.16 1
< 0.1%
5.19 1
< 0.1%
5.2 1
< 0.1%
ValueCountFrequency (%)
28.3 1
< 0.1%
27.01 1
< 0.1%
24.76 1
< 0.1%
23.95 1
< 0.1%
23.92 1
< 0.1%
23.67 1
< 0.1%
23.6 1
< 0.1%
23.57 1
< 0.1%
23.51 1
< 0.1%
23.4 1
< 0.1%

Trihalometanos
Real number (ℝ)

MISSING 

Distinct2403
Distinct (%)77.2%
Missing162
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean66.396281
Minimum0.74
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:06.572699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile39.55
Q155.8475
median66.62
Q377.3375
95-th percentile92.128
Maximum124
Range123.26
Interquartile range (IQR)21.49

Descriptive statistics

Standard deviation16.174983
Coefficient of variation (CV)0.24361278
Kurtosis0.23855383
Mean66.396281
Median Absolute Deviation (MAD)10.74
Skewness-0.082998291
Sum206758.02
Variance261.63007
MonotonicityNot monotonic
2024-09-22T17:52:06.677723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.33 4
 
0.1%
82.86 4
 
0.1%
70.83 4
 
0.1%
66.69 4
 
0.1%
68.91 4
 
0.1%
70.37 4
 
0.1%
64.21 4
 
0.1%
60.16 4
 
0.1%
63.7 4
 
0.1%
55.4 4
 
0.1%
Other values (2393) 3074
93.8%
(Missing) 162
 
4.9%
ValueCountFrequency (%)
0.74 1
< 0.1%
8.18 1
< 0.1%
8.58 1
< 0.1%
14.34 1
< 0.1%
15.68 1
< 0.1%
16.29 1
< 0.1%
17 1
< 0.1%
17.53 1
< 0.1%
17.92 1
< 0.1%
18.02 1
< 0.1%
ValueCountFrequency (%)
124 1
< 0.1%
120.03 1
< 0.1%
118.36 1
< 0.1%
116.16 1
< 0.1%
114.21 1
< 0.1%
114.03 1
< 0.1%
113.05 1
< 0.1%
112.62 1
< 0.1%
112.41 1
< 0.1%
112.06 1
< 0.1%

Turbidez
Real number (ℝ)

Distinct403
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9667308
Minimum1.45
Maximum6.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2024-09-22T17:52:06.778745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile2.6875
Q13.44
median3.955
Q34.5
95-th percentile5.22
Maximum6.74
Range5.29
Interquartile range (IQR)1.06

Descriptive statistics

Standard deviation0.78035425
Coefficient of variation (CV)0.19672478
Kurtosis-0.06281802
Mean3.9667308
Median Absolute Deviation (MAD)0.535
Skewness-0.0079106728
Sum12995.01
Variance0.60895276
MonotonicityNot monotonic
2024-09-22T17:52:06.881770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.76 23
 
0.7%
3.84 23
 
0.7%
4.59 23
 
0.7%
3.89 22
 
0.7%
4.4 22
 
0.7%
3.63 22
 
0.7%
4.27 22
 
0.7%
3.92 22
 
0.7%
4.24 21
 
0.6%
4.18 21
 
0.6%
Other values (393) 3055
93.3%
ValueCountFrequency (%)
1.45 1
< 0.1%
1.49 1
< 0.1%
1.5 1
< 0.1%
1.64 1
< 0.1%
1.66 1
< 0.1%
1.68 1
< 0.1%
1.69 1
< 0.1%
1.8 1
< 0.1%
1.81 1
< 0.1%
1.84 1
< 0.1%
ValueCountFrequency (%)
6.74 1
< 0.1%
6.49 2
0.1%
6.39 1
< 0.1%
6.36 1
< 0.1%
6.31 1
< 0.1%
6.23 1
< 0.1%
6.2 1
< 0.1%
6.1 1
< 0.1%
6.08 1
< 0.1%
6.07 1
< 0.1%

Potabilidad
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size188.9 KiB
NO
1998 
SI
1278 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6552
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 1998
61.0%
SI 1278
39.0%

Length

2024-09-22T17:52:06.972790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-22T17:52:07.042806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 1998
61.0%
si 1278
39.0%

Most occurring characters

ValueCountFrequency (%)
N 1998
30.5%
O 1998
30.5%
S 1278
19.5%
I 1278
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1998
30.5%
O 1998
30.5%
S 1278
19.5%
I 1278
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1998
30.5%
O 1998
30.5%
S 1278
19.5%
I 1278
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1998
30.5%
O 1998
30.5%
S 1278
19.5%
I 1278
19.5%

Interactions

2024-09-22T17:52:04.000756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-22T17:52:00.035648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.682793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.432961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.084107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.728251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.357618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.070772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:58.849382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.482524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.108664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.755809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.508977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.158123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.801270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.433629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.139787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:58.917398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.548539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.180680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.824825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.579994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.228140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.870286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.500644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.210803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:58.988413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.620555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.252697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.897841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.654011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-22T17:52:02.941302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.575660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.280819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.060429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.690571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.326713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.968857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.727027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.371171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.012318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.647677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.460859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.131445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.760586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.398729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.042874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.799042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.444187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.083334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.720693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.529874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.203461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.829602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.469745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.223914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.873059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.512203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.153556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.789708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.595889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.270476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.896617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.537760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.292930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.942075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.582218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.218570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.859725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:04.666905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.343493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:51:59.966632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:00.612778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:01.363945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.014091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:02.658235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.290587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-22T17:52:03.929739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-22T17:52:07.096324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Carbono_orgánicoCloraminasConductividadDurezaPotabilidadSulfatosSólidosTrihalometanosTurbidezpH
Carbono_orgánico1.000-0.0120.0210.0030.0140.0200.018-0.008-0.0250.044
Cloraminas-0.0121.000-0.017-0.0250.0780.037-0.0550.018-0.008-0.042
Conductividad0.021-0.0171.000-0.0330.000-0.0220.021-0.0040.0100.017
Dureza0.003-0.025-0.0331.0000.079-0.095-0.053-0.012-0.0130.116
Potabilidad0.0140.0780.0000.0791.0000.1510.0250.0000.0000.085
Sulfatos0.0200.037-0.022-0.0950.1511.000-0.154-0.031-0.0190.024
Sólidos0.018-0.0550.021-0.0530.025-0.1541.000-0.0200.029-0.075
Trihalometanos-0.0080.018-0.004-0.0120.000-0.031-0.0201.000-0.0280.005
Turbidez-0.025-0.0080.010-0.0130.000-0.0190.029-0.0281.000-0.049
pH0.044-0.0420.0170.1160.0850.024-0.0750.005-0.0491.000

Missing values

2024-09-22T17:52:04.756925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-22T17:52:04.878953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-22T17:52:04.968973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pHDurezaSólidosCloraminasSulfatosConductividadCarbono_orgánicoTrihalometanosTurbidezPotabilidad
0NaN204.8920791.327.30368.52564.3110.3886.992.96NO
13.72129.4218630.066.64NaN592.8915.1856.334.50NO
28.10224.2419909.549.28NaN418.6116.8766.423.06NO
38.32214.3722018.428.06356.89363.2718.44100.344.63NO
49.09181.1017978.996.55310.14398.4111.5632.004.08NO
55.58188.3128748.697.54326.68280.478.4054.922.56NO
610.22248.0728749.727.51393.66283.6513.7984.602.67NO
78.64203.3613672.094.56303.31474.6112.3662.804.40NO
8NaN118.9914285.587.80268.65389.3812.7153.933.60NO
911.18227.2325484.519.08404.04563.8917.9371.984.37NO
pHDurezaSólidosCloraminasSulfatosConductividadCarbono_orgánicoTrihalometanosTurbidezPotabilidad
32668.37169.0914622.757.55NaN464.5311.0838.444.91SI
32678.99215.0515921.416.30312.93390.419.9055.074.61SI
32686.70207.3217246.927.71304.51329.2716.2228.883.44SI
326911.4994.8137188.839.26258.93439.8916.1741.564.37SI
32706.07186.6626138.787.75345.70415.8912.0760.423.67SI
32714.67193.6847580.997.17359.95526.4213.8966.694.44SI
32727.81193.5517329.808.06NaN392.4519.90NaN2.80SI
32739.42175.7633155.587.35NaN432.0411.0469.853.30SI
32745.13230.6011983.876.30NaN402.8811.1777.494.71SI
32757.87195.1017404.187.51NaN327.4616.1478.702.31SI